Predicting Soccer Penalty Kick Direction Using Human Action Recognition

• URL: http://arxiv.org/abs/2507.12617v1
• Date: Wed, 16 Jul 2025 20:27:11 GMT
• Title: Predicting Soccer Penalty Kick Direction Using Human Action Recognition
• Authors: David Freire-Obregón, Oliverio J. Santana, Javier Lorenzo-Navarro, Daniel Hernández-Sosa, Modesto Castrillón-Santana,
• Abstract summary: This work presents a novel dataset of manually annotated soccer penalty kicks to predict shot direction based on pre-kick player movements.<n>We propose a deep learning classifier to benchmark this dataset that integrates HAR-based feature embeddings with contextual metadata.<n>We evaluate twenty-two backbone models across seven architecture families, achieving up to 63.9% accuracy in predicting shot direction.
• Score: 4.620715958859347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Action anticipation has become a prominent topic in Human Action Recognition (HAR). However, its application to real-world sports scenarios remains limited by the availability of suitable annotated datasets. This work presents a novel dataset of manually annotated soccer penalty kicks to predict shot direction based on pre-kick player movements. We propose a deep learning classifier to benchmark this dataset that integrates HAR-based feature embeddings with contextual metadata. We evaluate twenty-two backbone models across seven architecture families (MViTv2, MViTv1, SlowFast, Slow, X3D, I3D, C2D), achieving up to 63.9% accuracy in predicting shot direction (left or right), outperforming the real goalkeepers' decisions. These results demonstrate the dataset's value for anticipatory action recognition and validate our model's potential as a generalizable approach for sports-based predictive tasks.

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